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Background COVID-19 is one of the biggest pandemics in human history, along with other disease pandemics, such as the H1N1 influenza A, bubonic plague, and smallpox pandemics. This study is a small contribution that tries to find contrasted formulas to alleviate global suffering and guarantee a more manageable future. Objective In this study, a statistical approach was proposed to study the correlation between the incidence of COVID-19 in Spain and search data provided by Google Trends. Methods We assessed the linear correlation between Google Trends search data and the data provided by the National Center of Epidemiology in Spain—which is dependent on the Instituto de Salud Carlos III—regarding the number of COVID-19 cases reported with a certain time lag. These data enabled the identification of anticipatory patterns. Results In response to the ongoing outbreak, our results demonstrate that by using our correlation test, the evolution of the COVID-19 pandemic can be predicted in Spain up to 11 days in advance. Conclusions During the epidemic, Google Trends offers the possibility to preempt health care decisions in real time by tracking people's concerns through their search patterns. This can be of great help given the critical, if not dramatic need for complementary monitoring approaches that work on a population level and inform public health decisions in real time. This study of Google search patterns, which was motivated by the fears of individuals in the face of a pandemic, can be useful in anticipating the development of the pandemic.

The onset in the last 15 years of behavioral epidemiology has opened many new avenues for epidemiological modelers. In this manuscript we first review two classes of behavioral epidemiology models for vaccine preventable diseases, namely behaviour-implicit SIR models with prevalence-dependent vaccination (at birth and among older individuals), and prevalence-dependent contact rate. Subsequently, we briefly propose a general framework of behavior–dependent nonlinear and linear Forces of Infection (FoI) valid for a vast family of infectious diseases, and including delays and ‘epidemic memory’ effects. Finally and mainly, we develop a new general behavioral SIR model. This model combines the two aforementioned types of behavioral phenomena, previously considered only separately, into a single unified model for behavioral responses. The resulting model allows to develop a general phenomenological theory of the effects of behavioral responses within SIR models for endemic infections. In particular, the model allows to complete the picture about the complicate interplay between different behavioral responses acting on different epidemiological parameters in triggering sustained oscillations of vaccine coverage, risky behavior, and infection prevalence.

Highlights • Home isolation impacts the dynamics of infection spread via close contacts. • For multiple infections, the dynamic behaviour is interdependent. • The interdependence depends on the epidemiological parameters and symptom severity. • The effect on peak time and final size can be both positive and negative., Behavioral epidemiology, the field aiming to determine the impact of individual behavior on the spread of an epidemic, has gained increased recognition during the last few decades. Behavioral changes due to the development of symptoms have been studied in mono-infections. However, in reality, multiple infections are circulating within the same time period and behavioral changes resulting from contraction of one of the diseases affect the dynamics of the other. The present study aims at assessing the effect of home isolation on the joint dynamics of two infectious diseases, including co-infection, assuming that the two diseases do not confer cross-immunity. We use an age- and time- structured co-infection model based on partial differential equations. Social contact matrices, describing different mixing patterns of symptomatic and asymptomatic individuals are incorporated into the calculation of the age- and time-specific marginal forces of infection. Two scenarios are simulated, assuming that one of the diseases has more severe symptoms than the other. In the first scenario, people stay only at home when having symptoms of the most severe disease. In the second scenario, twice as many people stay at home when having symptoms of the most severe disease than when having symptoms of the other disease. The results show that the impact of home isolation on the joint dynamics of two infectious diseases depends on the epidemiological parameters and properties of the diseases (e.g., basic reproduction number, symptom severity). In case both diseases have a low to moderate basic reproduction number, and there is no home isolation for the less severe disease, the final size of the less severe disease increases with the proportion of symptomatic cases of the most severe disease staying at home, after an initial decrease. This counterintuitive result could be explained by a shift in the peak time of infection of the disease with the most severe symptoms, resulting in a smaller number of people with less contacts at the peak time of the other infection. When twice as many people stay at home when having symptoms of the most severe disease than when having symptoms of the other disease, increasing the proportion staying at home always reduces the final size of both diseases, and the number of co-infections. In conclusion, when providing advise if people should stay at home in the context of two or more co-circulating diseases, one has to take into account epidemiological parameters and symptom severity.

This study investigates cross-sectional and longitudinal associations between the diversity of leisure-time sport activities and the frequencies of low back pain (LBP) and neck-shoulder region pain (NSP) in twins, including a cross-sectional within-pair design to adjust for potential familial confounding. Finnish twins born in 1975–79 (FinnTwin16 study) reported participation in leisure-time sport activities at the mean ages of 17 (1992–96) (n = 5096, 54% females) and 34 years (2010−12) (n = 3731, 57% females). Diversity assessed as the number of sport activities was categorized as 1, 2, 3, 4, and ≥ 5, excluding inactive individuals. The frequencies of LBP (n = 3201) and NSP (n = 3207), reported at age 34, were categorized as never/seldom, monthly, or weekly pain. Cross-sectional and longitudinal individual-based associations between the number of sport activities and the frequency of LBP and NSP were investigated with multinomial logistic regression analyses, adjusting for multiple confounders. Cross-sectionally, participation in ≥5 sport activities, compared to 1 sport, was associated with significantly less weekly LBP (OR = 0.63, 95%CI = 0.43–0.90), but not with NSP. Longitudinally, participation in several sport activities in adolescence had no significant association with LBP or NSP in adulthood. Cross-sectional within-pair analyses were conducted among twin pairs discordant for LBP (n = 507) and NSP (n = 579). The associations between monozygotic and dizygotic twin pairs were similar in LBP-discordant pairs but differed within NSP-discordant pairs. Participation in ≥5 sport activities in adulthood may be associated with less weekly LBP, but not with monthly LBP or the frequency of NSP. However, within-pair analyses for NSP suggest confounding due to shared familial factors., Highlights • Participation in more sport activities is associated with less low back pain (LBP). • More sport activities is not associated with neck-shoulder pain (NSP). • Shared familial factors may confound the association between sport activities and NSP. • Number of sport activities in adolescence did not predict LBP or NSP in adulthood.

In this paper, we consider a SEIR epidemiological model with information--related changes in contact patterns. One of the main features of the model is that it includes an information variable, a negative feedback on the behavior of susceptible subjects, and a function that describes the role played by the infectious size in the information dynamics. Here we focus in the case of delayed information. By using suitable assumptions, we analyze the global stability of the endemic equilibrium point and disease--free equilibrium point. Our approach is applicable to global stability of the endemic equilibrium of the previously defined SIR and SIS models with feedback on behavior of susceptible subjects.

The spread of infectious diseases can be impacted by human behavior, and behavioral decisions often depend implicitly on a planning horizon—the time in the future over which options are weighed. We investigate the effects of planning horizons on epidemic dynamics. We developed an epidemiological agent-based model (along with an ODE analog) to explore the decision-making of self-interested individuals on adopting prophylactic behavior. The decision-making process incorporates prophylaxis efficacy and disease prevalence with the individuals’ payoffs and planning horizon. Our results show that for short and long planning horizons individuals do not consider engaging in prophylactic behavior. In contrast, individuals adopt prophylactic behavior when considering intermediate planning horizons. Such adoption, however, is not always monotonically associated with the prevalence of the disease, depending on the perceived protection efficacy and the disease parameters. Adoption of prophylactic behavior reduces the epidemic peak size while prolonging the epidemic and potentially generates secondary waves of infection. These effects can be made stronger by increasing the behavioral decision frequency or distorting an individual’s perceived risk of infection.

Background and objectives Differences in sociodemographic attributes and healthcare access may explain differences in regional sexually transmitted disease rates but don't fully explain why syphilis persists disproportionately in certain populations. Goal of this study To understand the behavioral epidemiology of syphilis, we conducted a social network analysis of persons with syphilis and their contacts and developed and applied a definition of core transmitters. Study design We interviewed 10 index persons with primary or secondary untreated syphilis and 80 of their named sexual and social contacts. Results Fourteen (16%) of 90 interviewed persons met the definition of core transmitters, 9 of whom had past or current syphilis. The other interviewed persons had only moderately risky behaviors. Seventy-eight (42%) of the network sexual contacts were connected directly or indirectly to a core transmitter. Conclusion This analysis suggests that syphilis transmission is maintained by a community with a small percentage of high-risk persons centrally placed amidst a larger group with moderately risky behavior.

The focus of the growing discipline of behavioral epidemiology (BE) of infectious diseases is on individual behavior as a key determinant of infection trajectories. This overview departs from the central, but static, role of human behavior in traditional mathematical models of infection to motivate the importance of including behavior into epidemiological models. Our aim is threefold. First, we attempt to motivate the historical and cultural background underpinning the BE revolution, focusing on the issue of rational opposition to vaccines as a natural endpoint of the changed relation between man and disease in modern industrialized countries. Second, we review those contributions, from both mathematical epidemiology and economics, that forerun the current “epidemic” of studies on BE. Last, we offer a more detailed overview of the current epidemic phase of BE studies and, still motivated by the issue of immunization choices, introduce some baseline ideas and models.

Hypertension is a major cause of stroke in the African-American community, and lack of control of hypertension appears to be common. Improving compliance to antihypertensive therapy in African-American stroke patients could have a significant impact on recurrent stroke rates. Little is known about factors affecting compliance in this community and which interventions would be effective in improving compliance. Health behavior models which assess the patient’s perception of stroke and hypertension, barriers to the desired behavior, perception of ability to perform the behavior, perception of others’ acceptance of the behavior and the patient’s behavioral stage could be used to tailor interventions to improve compliance. A plan to improve compliance should take into account the target population’s baseline rates of compliance, perception of need for intervention and risk factors for noncompliance.

The primary objective of this chapter is to suggest ideas that may be helpful for integrating the literatures of behavioral and psychosocial epidemiology, in the broader context of the study of health-related behaviors in older adulthood. The chapter is organized into four sections. The first section discusses the importance of understanding the health-related behaviors of older persons. The section reviews studies from the domain of behavioral epidemiology, which link lifestyle habits with the outcomes of mortality and morbidity. The topic of patterns among, and correlates of, health behaviors is also discussed.